2026-09-18T23:18:33Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1394492022-01-15T04:01:58Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Kraska, Tim
Wellens, Quentin
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2022-01-14T15:12:08Z
2022-01-14T15:12:08Z
2021-06
2021-06-17T20:14:47.221Z
https://hdl.handle.net/1721.1/139449
As more processes become data-driven, anyone should be able to gather insights into databases without needing to develop complex computer skills typically required for data analytics software. We propose to design new paradigms in which users rely on their own natural language to analyze and visualize data. To that end, we develop three different approaches (unsupervised, rule-based, and supervised) to infer formal specifications from natural language utterances. Contrary to most other work, we developed these approaches in a low-resource environment using synthetically generated training sets, rather than expensive and labor-intensive expert annotations or crowd-sourced examples. Finally, we conducted a study to compare our proposed paradigm to drag-and-drop mechanisms. Not only does our best-performing model, Alcurve, achieve an 86.3% test accuracy on real user input, it also enables users to be 30% more productive when solving analytical tasks, which further highlights the important improvements in usability language-based interfaces can provide.
M.Eng.
Massachusetts Institute of Technology
In Copyright - Educational Use Permitted
Copyright MIT
http://rightsstatements.org/page/InC-EDU/1.0/
Natural Language Interfaces for Data Analytics
Thesis
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Master
Master of Engineering in Electrical Engineering and Computer Science
Publication
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<Abstract>As more processes become data-driven, anyone should be able to gather insights into databases without needing to develop complex computer skills typically required for data analytics software. We propose to design new paradigms in which users rely on their own natural language to analyze and visualize data. To that end, we develop three different approaches (unsupervised, rule-based, and supervised) to infer formal specifications from natural language utterances. Contrary to most other work, we developed these approaches in a low-resource environment using synthetically generated training sets, rather than expensive and labor-intensive expert annotations or crowd-sourced examples. Finally, we conducted a study to compare our proposed paradigm to drag-and-drop mechanisms. Not only does our best-performing model, Alcurve, achieve an 86.3% test accuracy on real user input, it also enables users to be 30% more productive when solving analytical tasks, which further highlights the important improvements in usability language-based interfaces can provide.</Abstract>
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